bioRxiv Subject Collection: Neuroscience's Journal

Initiation site of experimentally-evoked spreading depolarizations influence tissue outcomes in a murine stroke model
Spreading depolarization waves (SDs) are implicated in secondary expansion of brain injuries and are the target for initial clinical intervention trials. However, the assumption that SD directly causes neuronal injury has been challenged by recent findings with experimentally-induced SD in stroke models. The current study addressed this controversy by examining whether stroke consequences are confounded by the precise location of experimental SD initiation. Focal ischemic lesions were generated by transient distal middle cerebral artery occlusion in male mice. Clusters of SDs (6 at 10-min intervals) were induced by either focal KCl application or optogenetic stimulation during occlusion. SDs were initiated either in regions close to the infarct core (penumbral-SD; <50% perfusion) or in less compromised tissue in the same hemisphere (remote-SD; >70% perfusion). Despite the fact that all SDs fully invaded stroke expansion areas, the location of experimental SD induction had significant effects on stroke outcomes measured 48 hours after reperfusion. Penumbral-SDs resulted in larger infarct expansion than seen in control stroke mice lacking experimentally-imposed SD. Conversely, remote-SDs led to significantly smaller infarcts than stroke alone. Laser speckle contrast imaging of blood flow in injury expansion areas showed enhanced hypoperfusion responses after penumbral-SDs and larger hyperemic responses after remote-SDs, suggesting that differential vascular responses could contribute to stroke outcomes. Overall, this study helps to reconcile different prior reports by showing that experimentally-induced SDs can either exacerbate or reduce stroke-induced injury depending on the SD initiation site and further strengthens evidence for injurious roles of SDs initiating in vulnerable brain tissue.

Prefrontal and Subcortical Value Representation during Explore-Exploit Decision-Making and Suicide Attempts
Background: This study aims to understand learning and decision-making in suicidal behavior by investigating temporal dynamics of reward value encoding in individuals with late-life depression and a history of suicide attempts. Methods: In a retrospective case-control study, 134 older adults (33 with depression and history of suicide attempts, 29 with depression and suicidal ideation but no past attempts, 32 with depression and no suicidal ideation/past attempts, and 40 psychiatrically healthy controls) completed an explore-exploit decision-making task during functional MRI (fMRI). Multilevel models of deconvolved fMRI time series, time-locked to trial events, interrogated whether temporal patterns of reward value encoding was associated with suicidal behavior. Results: Suicidal behavior in general was associated with blunted ventral PFC (vPFC) value signals, but profiles varied as a function of attempt lethality. Specifically, low-lethality suicide attempts and excessive behavioral shifts were associated with abolished phasic responses to value updates in the default network vPFC and its connected regions, including the striatum, amygdala, and hippocampus. Additionally, an unexpected pattern of sustained negative value responses was observed in vPFC and striatal control subregions, and hippocampus of high-lethality suicide attempters. Conclusions: Diverging patterns of decision-related responses may reflect different paths toward suicidal behavior. Impaired value updating in individuals with low-lethality suicide attempts suggests a failure to integrate recent outcomes alongside prior experience, potentially relating to over-reactivity to stressors and a lower threshold for suicide attempts. In contrast, increased control network responses to difficult choices in high-lethality attempters may underlie cognitive constriction and consideration of a narrow set of potential solutions.

An ion channel omnimodel for standardized biophysical neuron modelling
Biophysical neuron modeling is an indispensable tool in neuroscience research, with the combination of diverse ion channel kinetics and morphologies being used to explain various single-neuron properties and responses. Despite this, there is no standard way of formulating ion channel models, making it challenging to relate models to one another and experimental data. Here, we revive the idea of a standard model for ion channels based on the Hodgkin-Huxley formulation, and apply it to a recently curated database of ion channel models. We demonstrate that this standard formulation, which we refer to as an omnimodel, accurately fits the majority of voltage-gated models in the database (over 3,000 models). It produces similar, if not identical, responses to voltage-clamp protocols in simulations where the ion channel omnimodels were used. Importantly, the standard formalism enables easy comparison of models based on parameter settings. It can also be used to make new observations about the space of ion channel kinetics found in neurons. Furthermore, it facilitates the inference of ion channel parameters from the responses to standard protocols. We provide an interactive platform to compare and select channel models, and encourage the community to use this standard formulation whenever possible, to facilitate understanding and comparison among models.

Incentive Salience, not Psychomotor Sensitization or Tolerance, Drives Escalation of Cocaine Self-Administration in Heterogeneous Stock Rats
Sensitization and tolerance are two phenomena often studied independently despite overlapping neurobiological substrates. Each has extensive research showing their influence on the development and maintenance of addiction, but the degree to which they drive escalation in cocaine self-administration is poorly understood. Using self-administration, noncontingent methods, and pose-estimation, we demonstrate that incentive salience, not psychomotor sensitization or tolerance, drives the escalation of cocaine self-administration in heterogenous stock rats. Individual differences in psychomotor sensitization or tolerance were found to have no effect on cocaine intake. Incentive salience as measured by locomotion and active lever entrances per meter occurring before the self-administration session began (pre-lever) during Short Access was found to predict intake during Long Access. Both pre-lever locomotion and active lever entrances per meter were found to increase during Long Access and after two-to-three days of abstinence. Critically, rats with low pre-lever activity during Short Access escalated both their intake and pre-lever measures by the end of Long Access to levels comparable with high pre-lever activity rats who maintained their elevated responding. These findings support the notion that incentive salience during Short Access is a catalyst to escalated use and an early marker of addiction vulnerability. Moreover, they suggest that individuals initially resistant to incentive salience can, with sufficient exposure, become sensitized and escalate cocaine use to the same level as more susceptible individuals. Analysis of pre-lever activity offers a novel longitudinal behavioral marker to predict vulnerability and provides a framework for understanding individual trajectories of addiction.

Induction of cortical ON/OFF periods in awake mice fulfills sleep functions
Can animals obtain core benefits of sleep while remaining awake? In mammals, slow-wave sleep is characterized by synchronized neuronal activity alternating between ON and OFF periods. Slow-wave activity and synchrony reflect sleep need, are correlated with synaptic strength in cortical circuits, and promote synaptic down-selection and memory consolidation. To address the above question, we locally induced alternating ON/OFF periods during wakefulness using optogenetics in mice. This led to a local, ipsilateral reduction of slow-wave activity and synchrony during subsequent sleep and to reduced markers of synaptic strength. Moreover, bilateral induction of OFF periods over sensorimotor cortex during sleep deprivation restored memory consolidation. Thus, inducing ON/OFF activity during wakefulness is sufficient to reduce local sleep need and fulfills core functions of sleep.

BrAVe: a unified framework for 3D interactive and integrative analysis of cross-modal multi-scale brain atlas data in diverse species
The brain exhibits remarkable complexity across molecular, structural, and functional modalities spanning spatial scales from micro- to macro-levels1-3. Recent advances in brain mapping have yielded increasingly sophisticated multimodal, multi-scale atlases in diverse species4-26. However, integrative analysis across these modalities and scales remains hindered by methodological and tool limitations27. Here, we present BrAVe (BrainAtlas Viewer), an open-source, species-agnostic framework for 3D visualized and integrative analysis of brain atlas data across modalities and scales. BrAVe supports five core data types--volumetric images, parcellated regions, feature points, neuronal tracings, and networks--in standard formats (e.g., .nrrd, .stl, .csv., and .swc), accommodating multimodal and multi-scale datasets from flies to primates. For single-modality datasets, BrAVe provides a suite of analyses, with regional distribution profiling as a common feature. This is exemplified by its single-neuron morphology analysis, which enables quantitative characterization of geometry, topology, projection, clustering, and putative wiring. For multimodal datasets, BrAVe establishes spatially resolved correspondence across modalities within a common reference space, allowing the identification of molecularly defined neurons and the discovery of functionally associated circuits. BrAVe further bridges scales by matching neuronal reconstructions from light microscopy (LM) and electron microscopy (EM), enabling assignment of LM neuronal types for EM datasets, and leveraging EM synaptic connectivity to refine mesoscale wiring. Additional features include 3D image registration, scalable client-server data handling, and export of static and animated outputs. Combined with its intuitive interface and species compatibility, BrAVe offers a unified solution for integrative analysis of brain atlas datasets and empowers data-driven discovery in neuroscience research.

Trait motivation is associated with Fusiform face area Morphometry Evidence from a Chinese Youth Sample
Trait motivation is fundamental in shaping human behaviors. Previous studies have primarily focused on their impact on affective and motivational processing, with their role in perceptual processes less investigated. The present study takes face perception, a crucial and well-studied perceptual process, as a representative specimen to examine the perceptual effect of trait motivation. We investigated whether the behavioral activation system (BAS) and the behavioral inhibition system (BIS) were associated with structural characteristics of the inferior temporal face-selective regions as well as face recognition performance. With a sample of Chinese young adults (N = 264), voxel-based morphometry revealed that BIS scores correlated with greater gray matter volume in the fusiform face area. Further, a higher BIS score was associated with slightly better performance in face recognition. These findings provide novel evidence that trait motivation, particularly behavioral inhibition, is linked to both the structure and function of the face processing system. This underlines the intrinsic coupling between motivational and perceptual systems, blurring the presumed divide between affective and perceptual processes.

Dissecting novel object exploration: a fully automated homecage-based novel object recognition test
The novel object recognition test is a frequently used memory test in rodents. Due to its ethological nature, cross-species relevance, and specificity to testing hippocampal and parahippocampal function, it has been widely applied in basic and translational research. However, its implementation proves challenging due to multiple uncontrolled factors. Here, we describe a fully automated homecage-based novel object recognition test for assessing long-term object memory in mice. We present an empirically guided computational model to show the robustness of this approach despite ambiguity in defining exploratory behaviours. We show that mice preferentially explored novel compared to familiar objects after 24-hour and 7-day retention periods, starting to discern them while still a distance away. The findings were replicated across two facilities. Furthermore, the ability to recognise the novel object depends on the mouses prior interactions with the replaced object after 24 hours, but not after 7 days. Finally, we showed that external factors may introduce undesired exploration biases, which can be addressed using relative instead of absolute discrimination measures. The fully automated homecage-based object recognition test will improve standardisation, rigour, and reproducibility, as well as expand our understanding of the factors influencing object exploratory behaviours and object memory.

XIST Self-regulates its Association with THOC2 and the Nuclear Epigenetic Machinery via miR-186 in Alzheimer's disease
Alzheimer's disease (AD) shows a female bias, with about two-thirds of cases occurring in women; however, the underlying reasons remain unclear. This study uncovers the role of the long non-coding RNA (lncRNA) XIST, a key regulator of X-chromosome inactivation (XCI), in driving female-specific AD pathology. Analyses of single-nucleus RNA sequencing (snRNA-seq) data from human AD cortical tissues and in vitro AD models reveal a notable increase and abnormal cytoplasmic localization of XIST, a phenomenon not previously observed in neurodegeneration. Mechanistically, altered EZH2 levels dampen the trimethylation of histone H3 at lysine 27 (H3K27me3) on the inactive X chromosome, disrupting epigenetic silencing. This dysregulation impacts the RNA export factor subunit THOC2, whose levels are also elevated in AD. Cytoplasmic XIST acts as a competing endogenous RNA (ceRNA) by binding to miR-186-5p, rescuing EZH2 and THOC2 transcripts. This creates a positive feedback loop that sustains both nuclear epigenetic machinery and cytoplasmic post-transcriptional regulation by XIST. The miR-186/EZH2/THOC2/XIST axis offers a molecular framework for understanding the female-biased vulnerability in AD, linking XIST-driven epigenetic changes with nuclear RNA export pathways via cytoplasmic XIST. Disrupted EZH2-XIST interaction reduces H3K27me3 marks on the inactive X chromosome, while increased THOC2 promotes THOC2 (TREX) binding to XIST, maintaining the harmful feedback. Our findings suggest a novel mechanism through which XIST gets recruited to the nuclear export pathways in AD. These results position XIST as a key epigenetic and post-transcriptional regulator in female AD pathophysiology and suggest it could be a target for sex-specific therapeutic interventions.

Convergent information flows explain recurring firing patterns in cerebral cortex
Cortical population events, short-lived patterns of neuronal activity that recur with some consistency, are central to sensorimotor coordination. These reproducible firing patterns are often attributed to attractor dynamics, supported by strong mutual connectivity. However, using multi-modal datasets - including 2-photon imaging, electrophysiology, and electron microscopy - we show that these reproducible patterns do not involve strongly interconnected neurons. Instead, we show that cortical networks exhibit hierarchical modularity, with core neurons acting as high-information-flow nodes positioned at module interfaces. These cores funnel activity but lack structural signatures of pattern completion units expected in an attractor network. Using computational models, we find that distance-dependent connectivity is necessary and sufficient to generate the modularity and transient reproducible events observed in cortex. Our findings suggest that cortical networks are instead pre-configured to support sensorimotor coordination. This work redefines the structural and dynamical basis of cortical activity, highlighting the link between modular structure and function.

V1 interlaminar coherence decreases with interocular conflict
Resolving conflicting input from the two eyes is a fundamental challenge for the visual system. In the primary visual cortex (V1), such interocular conflict induces modest suppression of single neuron spiking, but the accompanying population-level dynamics remain poorly understood. Here we examined laminar multi-unit activity and interlaminar local field potential (LFP) coherence in macaque V1 during dichoptic stimulation and binocular rivalry flash suppression (BRFS). From laminar microelectrode recordings, we found that interocular conflict reliably reduces interlaminar coherence, particularly between granular and infragranular layers, suggesting altered temporal coordination across the cortical column. Strikingly, during BRFS, coherence remained reduced even when firing rates were unchanged. Moreover, interlaminar coherence is higher for perceptually dominant BRFS stimuli, indicating that coherence across V1 layers covaries with perceptual outcome in the absence of significant firing-rate differences. These findings show that the temporal dynamics of population coherence are a more stable signal of interocular conflict than spike rate modulation.

Mixture models for domain-adaptive brain decoding
A grand challenge in brain decoding is to develop algorithms that generalize across multiple subjects and tasks. Here, we developed a new computational framework to minimize negative transfer for domain-adaptive brain decoding by reframing source selection as a mixture model parameter estimation problem, allowing each source subject to contribute through a continuous mixture weight rather than being outright included or excluded. To compute these weights, we developed a novel convex optimization algorithm based on the Generalized Method of Moments. By using model performance metrics as the generalized moment functions in our GMM optimization, our algorithm also provides theoretical guarantees that the mixture weights are an optimal approximation of the importance weights that underlie domain adaptation theory. When tested on a large-scale brain decoding dataset (n=105 subjects), our new mixture model weighting framework achieved state-of-the-art performance--increasing accuracy up to 2.5% over baseline fine-tuning, double the performance gain compared to previous research in supervised source selection. Notably, these improvements were achieved using significantly less training data (i.e., 62% smaller effective sample sizes), suggesting that our performance gains stem from reduced negative transfer. Collectively, this research advances toward a more principled and generalizable brain decoding framework, laying the mathematical foundation for scalable brain-computer interfaces and other applications in computational neuroscience.

Cold sensing by a glutamate receptor drives avoidance behavior in Drosophila larvae
The ability to sense and avoid noxious environments is essential for animal survival; yet, how this is achieved at the behavioral, neuronal, and molecular levels is not well understood. Here, we use Drosophila larvae as a model to investigate how animals sense and avoid cold temperatures. By implementing custom-built thermoelectric devices capable of delivering rapid and precise thermal stimuli, we find that cold delivered to the larval head evokes robust escape behavioral responses. We identify a group of head-located cold-sensitive neurons as necessary and sufficient for such avoidance responses. We further demonstrate that the kainate-type glutamate receptor Clumsy acts as a novel cold sensor required for head cold sensitivity. Knockdown of Clumsy in head cold-sensing neurons suppresses their cold sensitivity. Heterologous expression of Clumsy confers cold sensitivity. Our results show that Drosophila larvae have evolved the capacity to detect and avoid cold temperatures through a previously uncharacterized cold-sensing mechanism.

Effective computations for hippocampal place cell phenomena in sparse untrained random networks
The mammalian brain processes experience-dependent spatial information through poorly-understood network mechanisms thought to depend on particular network connectivity patterns and activity-dependent synaptic plasticity. However, dedicated input connections that learn to shape information about place cannot easily explain many rodent hippocampal place cell phenomena. For example, representational drift notwithstanding, the discharge of each place cell maps to specific locations in a fixed environment, but the discharge of most cells remap to distinct independent locations across environments, despite the fact that most sub-second cofiring relationships amongst hippocampal neuron pairs persist across environments. Whereas some models of hippocampal spatial information processing rely on the dedicated input connections of a for-purpose connectome (ignoring remapping, representational drift, and maintained cofiring), other models use synaptic plasticity implemented by learning rules to alter random input connections, but struggle with either limited capacity, representational drift, and/or biological implausibility. Here, using a randomly tuned network with feedback inhibition, we examine whether the assumptions of a specific connectome and learning-implemented synaptic plasticity are necessary for diverse place cell phenomena. We find that the random network with non-plastic connections accounts for positional tuning, single place fields in small spaces and multiple place fields in large spaces, mixed selectivity, and remapping, amongst other place cell phenomena. This requires excitatory activity to be sparse and organized across stimuli by divisive normalization. Enabling synaptic plasticity only at the network connections (not at network inputs) accounts for additional place cell phenomena including overdispersion, representational drift, and memory tagging. We show by simulations and analytically that DivSparse, a random network with sparsifying inhibition can explain many features of place cell network activity, suggesting that simple biologically-plausible architectures can realize representations of spatial experience that are robust, flexible, and spontaneous.

Synaptic histamine shapes the neurocomputational dynamics of human learning
Histamine was the first canonical monoamine identified in the mammalian brain 1-6, yet arguably remains the least understood in its mechanistic contributions to human behaviour. Using a first-in-class causal probe (H3R inverse agonist pitolisant), we show how increased synaptic histamine shapes offline and online temporal-hippocampal dynamics, sustaining learning-related activity and polarising retrieval computations. At a broader level, elevated histamine adaptively shifts neurocomputational strategy under high cognitive load, while stabilising value updates during aversive reinforcement learning. These findings uncover a mechanistically grounded influence of this underexplored system on human neurocomputation, supporting its therapeutic potential in psychiatry.

A molecular convergence in the triad of Parkinson's disease, depressive disorder and gut health is revealed by the inflammation-miRNA axis
Background: Parkinson's disease (PD) is a multisystem disorder frequently comorbid with non-motor symptoms like depressive disorder (DD) and gastrointestinal (GI) dysfunction. Chronic neuroinflammation and disruption of the gut-brain axis are implicated as shared pathological drivers, but the precise molecular mechanisms connecting these conditions remain elusive. We hypothesized that a common microRNA (miRNA)-mediated inflammatory profile underlies this clinical triad, representing a point of pathological convergence. Methods: We analyzed the expression of a panel of inflammatory bowel disease (IBD)-associated miRNAs, key inflammatory markers, and glial response in postmortem brain tissue (dorsolateral prefrontal cortex and caudate nucleus) from patients with PD, DD, and matched healthy controls. To investigate causality and gut-brain axis involvement, two mouse models were used: (i) PD-associated -synucleinopathy was induced in dorsal raphe serotonin (5-HT) neurons; and (ii) DD-like based on corticosterone (CORT)-induced stress. Mice were assessed for depressive-like behaviors and GI dysmotility, and their brain (medial prefrontal cortex and caudate-putamen) and ileum tissues were analyzed for the same molecular markers. Results: We identified a conserved miRNA pattern in the brains of both PD and DD patients, characterized by the significant downregulation of miR-199a-5p and miR-219a-5p and the upregulation of miR-200a-3p. This dysregulation was strongly associated with a pro-inflammatory state, as evidenced by increased expression of TNF, IFN-{gamma}, and NF{kappa}B1, as well as changes in the glial response. Mice with -synucleinopathy in the 5-HT system exhibited a depression-like phenotype and reduced intestinal motility, accompanied by increased Iba1 and GFAP signal. Comparable effects were observed in mice subjected to CORT-induced stress. Notably, the same pattern of miRNAs and inflammatory cytokines observed in the human brain was replicated in the brain and ileum of DD-PD-like mice, providing direct evidence of a parallel pathological process spanning the gut-brain axis. Conclusion: This study identifies a specific miRNA-inflammatory axis as a common molecular mechanism connecting the pathophysiology of PD, DD, and gut dysfunction. This pattern represents a critical point of convergence that drives a shared, bidirectional inflammatory cascade along the gut-brain axis. Targeting this miRNA triad could provide a new therapeutic approach for addressing the motor, psychiatric, and GI symptoms of these interconnected disorders simultaneously.

Hedonic experiences emerge from an orchestrated balance of synergistic and redundant information processing
Ketamine exerts rapid-acting, pro-hedonic effects, yet its precise mechanism remains elusive. Here, we present behavioral and fMRI data from a randomized, placebo-controlled crossover study in 38 healthy participants investigating ketamine's sub-acute effects on multivariate information-processing during music-evoked peak hedonic experiences. Leveraging information-theoretical measures, our findings indicate that hedonic experiences depend on a distinct global (as measured by O-Information) and local (as measured by integrated information) balance between redundant - information shared across nodes - and synergistic - information emerging from joint interactions - processes. As hedonic intensity rises, neural dynamics shift toward greater synergy; with the one exception of a deliberate increase in redundancy particularly for key sensory information to ensure reliable transmission of and access to critical external information for subsequent hedonic processing. In contrast, ketamine's sub-acute pro-hedonic effects arise potentially from enhancing redundant dynamics at rest, boosting the brain's capability to robustly represent and access critical internal information, and thus, fostering an environment optimized to amplify the phenomenological hedonic experience, while simultaneously allowing for more efficient information integration.